High Precision Prediction Method for Dam Deformation by Integrating Adaptive Wavelet Threshold and Informer

Accurate deformation forecasting is essential for concrete dam safety monitoring, yet real-world deformation series are often nonlinear, nonstationary, and contaminated by noise, missing values, and outliers, which limits the performance of traditional prediction models. This paper proposes a high-precision forecasting framework that integrates variational mode decomposition (VMD), adaptive wavelet-threshold denoising, and the informer model. First, VMD decomposes complex deformation signals into intrinsic mode functions to mitigate mode mixing and enhance frequency-specific interpretability. Then, an adaptive wavelet-threshold strategy is introduced, where the threshold is dynamically adjusted using signal-to-noise ratio (SNR) guidance and Stein's unbiased risk estimate (SURE), enabling effective noise suppression while preserving informative deformation patterns under nonstationary conditions. Finally, a multichannel informer architecture is employed to fuse multiscale components and capture long-range dependencies efficiently via ProbSparse self-attention. Experiments on deformation monitoring datasets from multiple concrete dams demonstrate that the proposed VMD-AWT-informer consistently outperforms mainstream baselines across forecasting horizons. For instance, at a 30-day horizon, the proposed method achieves an RMSE of 0.55 mm, reducing errors by 23.6% compared with VMD-LSTM and by 55.6% compared with SVR, while maintaining strong goodness of fit (R2=0.887). Robustness tests further confirm improved stability under noisy, incomplete, and outlier-corrupted inputs. These results indicate that the proposed framework provides an effective and practical tool for long-horizon dam deformation prediction and early warning.